A probability is a number from -1 to +1 inclusive that measures one's belief than an even resulting from an experiment will occur

The complement rule states that the probability of an event not occurring is equal to one minus the probability of its occurrence

In stratified sampling, a population is divided into strata using naturally occurring geographic or other boundaries. Then strata are randomly selected and a random sample is collected from each strata

If the size of a sample equals the size of the population, we would not expect any error in estimating the population parameter

We can expect some difference between sample statistics and the corresponding population parameters. This difference is called the sampling error

A Sampling distribution of the means is the probability distribution consisting of a list of all possible sample means of a given sample size selected from a population and the probability of occurrence associated with each sample mean

The central limit theorem implies that sample of size one or two are adequate to estimate population parameters

If a population is not normally distributed, the sampling distribution of the sample means tends to approximate a normal distribution

Based on the sampling distribution of the means and the central limit theorem, the sample mean can be used as a good estimator of the population mean, assuming that the size of the sample is sufficiently large.

An estimate of the population mean based on a large sample is less reliable than an estimate made using a small sample

If the sample size keeps getting larger and larger and finally equals the size of the population, there would be no error in predicting the population mean because the sample size and the size of the population would be the same